Strawberry Detection Using a Heterogeneous Multi-Processor Platform
Samuel Brandenburg, Pedro Machado, Nikesh Lama, T.M. McGinnity

TL;DR
This paper presents a method for strawberry detection using YOLOv3 CNN accelerated on a heterogeneous FPGA platform, achieving five times faster processing with 78.3% accuracy for precision farming robots.
Contribution
It introduces a hybrid FPGA-based acceleration approach for deep learning in precision farming, improving speed without sacrificing significant accuracy.
Findings
Fivefold speed improvement on FPGA compared to CPU
78.3% detection accuracy on test images
Effective application of YOLOv3 in embedded systems
Abstract
Over the last few years, the number of precision farming projects has increased specifically in harvesting robots and many of which have made continued progress from identifying crops to grasping the desired fruit or vegetable. One of the most common issues found in precision farming projects is that successful application is heavily dependent not just on identifying the fruit but also on ensuring that localisation allows for accurate navigation. These issues become significant factors when the robot is not operating in a prearranged environment, or when vegetation becomes too thick, thus covering crop. Moreover, running a state-of-the-art deep learning algorithm on an embedded platform is also very challenging, resulting most of the times in low frame rates. This paper proposes using the You Only Look Once version 3 (YOLOv3) Convolutional Neural Network (CNN) in combination with…
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Taxonomy
TopicsSmart Agriculture and AI · Plant Virus Research Studies · Plant Disease Management Techniques
